The Role of AI in Insurance
AI has sparked a new era of innovation and efficiency in insurance, driven by a rapidly evolving risk landscape, unprecedented technological advancements and increasing customer demand for more personalized service. In this article, we’ll explore the dynamics of this transformation, providing an in-depth view of the factors fueling the rise of AI in insurance. We’ll also look at several practical examples of how this technology is being used by insurers to enhance their operational efficiency, manage risks and deliver personalized services.
Factors Driving adoption of AI in insurance
AI-enabled solutions are helping insurers grow their market share and improve profitability. Several catalysts are fueling the adoption of this technology in the insurance sector.
Rapid advances in machine learning and AI technologies
The explosive growth of AI and machine learning has provided new opportunities to extract value from data. One example is large language modeling (LLM), a branch of artificial intelligence capable of intelligently processing data contained in documents. Using LLMs, insurers can accelerate their claims processes and detect fraudulent claims with improved accuracy. Another example is generative AI, which brings together data, tools, and functions and then applies reasoning power to provide an answer. While generative AI is in its infancy, it holds significant potential for the insurance industry.
Availability of larger and more diverse data sources
The sharp rise in the quantity and variety of data sources means more raw material is available to mine for value. Third-party data has also taken on an increasingly important role, as insurers are supplementing their own data with consumer credit data, marketing data, social media posts, information on purchasing behavior, criminal records, past insurance claims and weather data. Third-party data helps insurers improve their policy underwriting, risk modeling, claims processing, marketing practices and more.
Demand for enhanced customer service
AI-powered solutions such as chatbots and virtual assistants are enabling insurers to meet customer demand for prompt, personalized support that’s available 24/7. Natural language processing (NLP), a type of AI that allows machines to understand and respond to written and spoken language, is powering front-line customer service solutions that provide always-on basic support to free human agents to focus on more complex customer issues.
Capabilities of the modern cloud data platform
Modern cloud data platforms such as Snowflake offer an efficient, affordable way to store and process massive quantities of data. With support for structured, semi-structured and unstructured data, insurers can use diverse data sets for their AI model training and refinement. Elastically scalable compute power allows teams to effectively manage their AI and ML workflows.
Applications of AI in insurance
As an early adopter of data analysis, the insurance industry is well-positioned to maximize AI’s potential. While AI has many powerful use cases for improving the insurance industry, let’s look at seven that specifically highlight AI’s role in shaping the future of insurance.
More efficient claims processing
AI-powered processes streamline claims processing workflows, significantly reducing the time it takes for insurers to process and pay out claims. Artificial intelligence can analyze claim forms, documents and visual data such as images and video, and automatically prioritize incoming claims based on their complexity and urgency.
Improved risk management
AI-powered predictive models analyze vast amounts of data from many different sources, empowering insurers to forecast trends and identify potential risks with increased accuracy. These capabilities allow insurers to proactively adjust their strategies and pricing to better manage risk.
AI algorithms can create customizable coverage based on the needs and risk factors of individuals. By analyzing individual customer data, insurers can generate custom insurance products and individualized pricing, often within minutes. An example is the use of telematics data gathered from an app installed on a driver’s smartphone or an onboard vehicle sensor. By collecting data on the mileage and driving habits of individual drivers, insurance companies can provide customized driving feedback, safe driving discounts and offer more competitive, risk-based pricing. The ability to issue real-time quotes with highly personalized pricing and converge options can give insurers a competitive advantage.
Superior customer service
NLP-enabled virtual agents and chatbots enhance customer interactions with intelligent responses to customer service queries and quick access to policy information. Around-the-clock access to customer support improves customer satisfaction and reduces the burden placed on human agents. AI is also used for sentiment analysis, the process of identifying and categorizing emotions expressed in digital text, including online reviews, social media posts and customer service chat messages. Using sentiment analysis, insurers can quickly identify and correct customer service and support issues.
Advanced fraud detection
Fraudulent property and casualty losses are a major source of lost revenue for insurers. From healthcare to business insurance use cases, AI-enabled predictive modeling and anomaly detection identify difficult-to-spot patterns and inconsistencies that indicate a claim is fraudulent.
Cost-savings through automation
AI-driven systems are incredibly useful for completing time-consuming manual tasks quickly and efficiently. The creation of highly automated systems reduces the opportunity for human error and frees up experienced underwriters and claims processors to focus on more complex and higher-level work. An example of this type of automation is the use of NLP to extract key information from claims documentation.
Move your AI-driven insurance initiatives forward with Snowflake
With the power of the Financial Services Data Cloud, insurers are leveraging data technology to better assess risk, optimize pricing and policies and launch new products with data science and ML-enabled automation. Snowflake gives insurers the ability to leverage LLMs and industry-specific use cases with an LLM-safe security perimeter around corporate data. With a single data platform for scaling multiple workloads, the insurance industry is well-positioned for future innovation.
See our on-demand webinar, The Emergence of Generative AI and LLMs in Insurance: A Review of Insurance Industry Adoption and Applications to discover how organizations are building AI strategies with cloud and enterprise data initiatives.